Plot Types¶

This notebook demonstrates all the plot types available in xarray_plotly.

In [1]:

import numpy as np
import pandas as pd
import xarray as xr

from xarray_plotly import config, xpx

config.notebook()  # Configure Plotly for notebook rendering

import numpy as np import pandas as pd import xarray as xr from xarray_plotly import config, xpx config.notebook() # Configure Plotly for notebook rendering

Sample Data¶

Let's create some sample data to work with:

In [2]:

np.random.seed(42)

# Time series data
da_ts = xr.DataArray(
    np.random.randn(30, 3).cumsum(axis=0),
    dims=["time", "category"],
    coords={
        "time": pd.date_range("2024-01-01", periods=30),
        "category": ["A", "B", "C"],
    },
    name="value",
)

# 2D grid data
da_2d = xr.DataArray(
    np.random.rand(20, 30),
    dims=["lat", "lon"],
    coords={
        "lat": np.linspace(-90, 90, 20),
        "lon": np.linspace(-180, 180, 30),
    },
    name="temperature",
)

# Categorical data
da_cat = xr.DataArray(
    np.random.rand(4, 3) * 100,
    dims=["product", "region"],
    coords={
        "product": ["Widget", "Gadget", "Gizmo", "Thingamajig"],
        "region": ["North", "South", "West"],
    },
    name="sales",
)

np.random.seed(42) # Time series data da_ts = xr.DataArray( np.random.randn(30, 3).cumsum(axis=0), dims=["time", "category"], coords={ "time": pd.date_range("2024-01-01", periods=30), "category": ["A", "B", "C"], }, name="value", ) # 2D grid data da_2d = xr.DataArray( np.random.rand(20, 30), dims=["lat", "lon"], coords={ "lat": np.linspace(-90, 90, 20), "lon": np.linspace(-180, 180, 30), }, name="temperature", ) # Categorical data da_cat = xr.DataArray( np.random.rand(4, 3) * 100, dims=["product", "region"], coords={ "product": ["Widget", "Gadget", "Gizmo", "Thingamajig"], "region": ["North", "South", "West"], }, name="sales", )

Line Plot¶

Best for time series and continuous data:

In [3]:

fig = xpx(da_ts).line(title="Line Plot")
fig

fig = xpx(da_ts).line(title="Line Plot") fig

With markers¶

In [4]:

fig = xpx(da_ts).line(markers=True, title="Line Plot with Markers")
fig

fig = xpx(da_ts).line(markers=True, title="Line Plot with Markers") fig

Bar Chart¶

Best for comparing categorical data:

In [5]:

fig = xpx(da_cat).bar(title="Stacked Bar Chart")
fig

fig = xpx(da_cat).bar(title="Stacked Bar Chart") fig

Grouped bars¶

In [6]:

fig = xpx(da_cat).bar(barmode="group", title="Grouped Bar Chart")
fig

fig = xpx(da_cat).bar(barmode="group", title="Grouped Bar Chart") fig

Area Chart¶

Best for showing composition over time:

In [7]:

# Use absolute values for stacking to make sense
da_positive = xr.DataArray(
    np.abs(np.random.randn(30, 3)) * 10,
    dims=["time", "source"],
    coords={
        "time": pd.date_range("2024-01-01", periods=30),
        "source": ["Solar", "Wind", "Hydro"],
    },
    name="energy",
)

fig = xpx(da_positive).area(title="Stacked Area Chart")
fig

# Use absolute values for stacking to make sense da_positive = xr.DataArray( np.abs(np.random.randn(30, 3)) * 10, dims=["time", "source"], coords={ "time": pd.date_range("2024-01-01", periods=30), "source": ["Solar", "Wind", "Hydro"], }, name="energy", ) fig = xpx(da_positive).area(title="Stacked Area Chart") fig

Scatter Plot¶

Best for showing relationships between variables:

In [8]:

fig = xpx(da_ts).scatter(title="Scatter Plot")
fig

fig = xpx(da_ts).scatter(title="Scatter Plot") fig

Dimension vs Dimension (Geographic style)¶

You can plot one dimension against another, with values shown as color:

In [9]:

fig = xpx(da_2d).scatter(x="lon", y="lat", color="value", title="Lat/Lon Scatter")
fig

fig = xpx(da_2d).scatter(x="lon", y="lat", color="value", title="Lat/Lon Scatter") fig

Box Plot¶

Best for showing distributions:

In [10]:

# Create data with more samples
da_dist = xr.DataArray(
    np.random.randn(100, 4) + np.array([0, 1, 2, 3]),
    dims=["sample", "group"],
    coords={"group": ["Control", "Treatment A", "Treatment B", "Treatment C"]},
    name="response",
)

fig = xpx(da_dist).box(title="Box Plot")
fig

# Create data with more samples da_dist = xr.DataArray( np.random.randn(100, 4) + np.array([0, 1, 2, 3]), dims=["sample", "group"], coords={"group": ["Control", "Treatment A", "Treatment B", "Treatment C"]}, name="response", ) fig = xpx(da_dist).box(title="Box Plot") fig

Heatmap (imshow)¶

Best for 2D grid data:

In [11]:

fig = xpx(da_2d).imshow(title="Heatmap")
fig

fig = xpx(da_2d).imshow(title="Heatmap") fig

With different colorscale¶

In [12]:

fig = xpx(da_2d).imshow(
    color_continuous_scale="Viridis",
    title="Heatmap with Viridis colorscale",
)
fig

fig = xpx(da_2d).imshow( color_continuous_scale="Viridis", title="Heatmap with Viridis colorscale", ) fig

Faceting¶

All plot types support faceting to create subplot grids:

In [13]:

# 3D data for faceting
da_3d = xr.DataArray(
    np.random.randn(30, 3, 2).cumsum(axis=0),
    dims=["time", "city", "scenario"],
    coords={
        "time": pd.date_range("2024-01-01", periods=30),
        "city": ["NYC", "LA", "Chicago"],
        "scenario": ["Low", "High"],
    },
    name="value",
)

fig = xpx(da_3d).line(
    facet_col="city",
    facet_row="scenario",
    title="Faceted Line Plot",
)
fig

# 3D data for faceting da_3d = xr.DataArray( np.random.randn(30, 3, 2).cumsum(axis=0), dims=["time", "city", "scenario"], coords={ "time": pd.date_range("2024-01-01", periods=30), "city": ["NYC", "LA", "Chicago"], "scenario": ["Low", "High"], }, name="value", ) fig = xpx(da_3d).line( facet_col="city", facet_row="scenario", title="Faceted Line Plot", ) fig

Animation¶

Create animated plots by assigning a dimension to animation_frame:

In [14]:

# Create monthly data
da_monthly = xr.DataArray(
    np.random.rand(12, 4) * 100,
    dims=["month", "product"],
    coords={
        "month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", 
                  "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
        "product": ["A", "B", "C", "D"],
    },
    name="sales",
)

fig = xpx(da_monthly).bar(
    x="product",
    animation_frame="month",
    title="Monthly Sales (Animated)",
    range_y=[0, 120],
)
fig

# Create monthly data da_monthly = xr.DataArray( np.random.rand(12, 4) * 100, dims=["month", "product"], coords={ "month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"], "product": ["A", "B", "C", "D"], }, name="sales", ) fig = xpx(da_monthly).bar( x="product", animation_frame="month", title="Monthly Sales (Animated)", range_y=[0, 120], ) fig